The procedure iterates on the population rows and the features corresponding to each solution are selected from the starting data matrix. Afterwards, the reduced data matrix is passed as input to one of the considered learning models, i.e., a SVM [1]} with linear kernel using a 10-fold cross validation as the supervised model, and a K-means clustering algorithm [2]} with City Block distance [3]} as the unsupervised one. Then the fitness function of interest is computed using a wrapper [4]} approach, obtaining a fitness value for each solution. The best one is stored for each GA generation.
